Dr Spyros Samothrakis
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Email
ssamot@essex.ac.uk -
Telephone
+44 (0) 1206 872683
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Location
parkside block c2, Colchester Campus
Profile
Qualifications
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2014, PhD Computer Science,University of Essex
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2007, MSc Intelligent Systems, University of Sussex
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2003, BSc Computer Science, University of Sheffield
Research and professional activities
Research interests
Reinforcement Learning
Machine Learning
Neural Networks
Role Playing Games
Teaching and supervision
Current teaching responsibilities
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Data Science and Decision Making (CE888)
Previous supervision
Degree type: Master of Science
Awarded date: 5/10/2016
Publications
Journal articles (15)
Samothrakis, S., (2018). Viewpoint: Artificial intelligence and labour. IJCAI International Joint Conference on Artificial Intelligence. 2018-July, 5652-5655
Samothrakis, S., (2018). Kathryn E. Merrick: Computational models of motivation for game-playing agents: Springer, 2016, 213 pp, ISBN: 978-3-319-33457-8. Genetic Programming and Evolvable Machines. 19 (4), 567-568
Samothrakis, S., (2018). Kathryn E. Merrick: Computational models of motivation for game-playing agents - Springer, 2016, 213 pp, ISBN: 978-3-319-33457-8.. Genetic Programming and Evolvable Machines. 19, 567-568
Tom Vodopivec, Samothrakis, S. and Brank Ster, (2017). On monte carlo tree search and reinforcement learning. The Journal of Artificial Intelligence Research. 60, 881-936
Samothrakis, S., Fasli, M., Perez, D. and Lucas, S., (2017). Default policies for global optimisation of noisy functions with severe noise. Journal of Global Optimization. 67 (4), 893-907
Samothrakis, S., Perez, D., Lucas, SM. and Rohlfshagen, P., (2016). Predicting Dominance Rankings for Score-Based Games. IEEE Transactions on Computational Intelligence and AI in Games. 8 (1), 1-12
Perez-Liebana, D., Samothrakis, S., Togelius, J., Schaul, T., Lucas, SM., Couetoux, A., Lee, J., Lim, C-U. and Thompson, T., (2016). The 2014 General Video Game Playing Competition. IEEE Transactions on Computational Intelligence and AI in Games. 8 (3), 229-243
Perez, D., Mostaghim, S., Samothrakis, S. and Lucas, SM., (2015). Multiobjective Monte Carlo Tree Search for Real-Time Games. IEEE Transactions on Computational Intelligence and AI in Games. 7 (4), 347-360
Samothrakis, S. and Fasli, M., (2015). Emotional Sentence Annotation Helps Predict Fiction Genre. PLOS ONE. 10 (11), e0141922-e0141922
Perez, D., Powley, EJ., Whitehouse, D., Rohlfshagen, P., Samothrakis, S., Cowling, PI. and Lucas, SM., (2014). Solving the Physical Traveling Salesman Problem: Tree Search and Macro Actions. IEEE Transactions on Computational Intelligence and AI in Games. 6 (1), 31-45
Perez, D., Togelius, J., Samothrakis, S., Rohlfshagen, P. and Lucas, SM., (2014). Automated Map Generation for the Physical Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation. 18 (5), 708-720
Samothrakis, S., Lucas, S., Runarsson, T. and Robles, D., (2013). Coevolving Game-Playing Agents: Measuring Performance and Intransitivities. IEEE Transactions on Evolutionary Computation. 17 (2), 213-226
Friston, K., Samothrakis, S. and Montague, R., (2012). Active inference and agency: optimal control without cost functions. Biological Cybernetics. 106 (8-9), 523-541
Browne, CB., Powley, E., Whitehouse, D., Lucas, SM., Cowling, PI., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S. and Colton, S., (2012). A Survey of Monte Carlo Tree Search Methods. IEEE Transactions on Computational Intelligence and AI in Games. 4 (1), 1-43
Samothrakis, S., Robles, D. and Lucas, S., (2011). Fast Approximate Max-n Monte Carlo Tree Search for Ms Pac-Man. IEEE Transactions on Computational Intelligence and AI in Games. 3 (2), 142-154
Conferences (20)
Samothrakis, S., (2018). Viewpoint: Artificial Intelligence and Labour.
Alshahrani, M., Samothrakis, S. and Fasli, M., (2017). Word mover's distance for affect detection
Abdullahi, U., Samothrakis, S. and Fasli, M., (2017). Counterfactual domain adversarial training of neural networks
Alshahrani, M., Samothrakis, S., Fasli, M. and IEEE, (2017). Word Mover's Distance for Affect Detection
Abdullahi, UI., Samothrakis, S., Fasli, M. and IEEE, (2017). Counterfactual Domain Adversarial Training of Neural Networks
Samothrakis, S., Vodopivec, T., Fairbank, M. and Fasli, M., (2017). Convolutional-Match Networks for Question Answering
Perez-Liebana, D., Samothrakis, S., Togelius, J., Lucas, SM. and Schaul, T., (2016). General video game AI: Competition, challenges, and opportunities
Perez-Liebana, D., Samothrakis, S., Togelius, J., Schaul, T. and Lucas, SM., (2016). Analyzing the robustness of general video game playing agents
Samothrakis, S., Vodopivec, T., Fasli, M. and Fairbank, M., (2016). Match memory recurrent networks
Samothrakis, S., Perez-Liebana, D., Lucas, SM. and Fasli, M., (2015). Neuroevolution for General Video Game Playing
Perez, D., Powley, E., Whitehouse, D., Samothrakis, S., Lucas, S. and Cowling, PI., (2014). The 2013 Multi-objective Physical Travelling Salesman Problem Competition
Perez, D., Samothrakis, S. and Lucas, S., (2014). Knowledge-based fast evolutionary MCTS for general video game playing
Samothrakis, S., Roberts, SA., Perez, D. and Lucas, SM., (2014). Rolling horizon methods for games with continuous states and actions
Lucas, SM., Samothrakis, S. and Pérez, D., (2014). Fast Evolutionary Adaptation for Monte Carlo Tree Search
Perez, D., Samothrakis, S. and Lucas, S., (2013). Online and offline learning in multi-objective Monte Carlo Tree Search
Perez, D., Samothrakis, S., Lucas, S. and Rohlfshagen, P., (2013). Rolling horizon evolution versus tree search for navigation in single-player real-time games
Ashlock, D., Ashlock, W., Samothrakis, S., Lucas, S. and Lee, C., (2012). From competition to cooperation: Co-evolution in a rewards continuum
Samothrakis, S. and Lucas, S., (2011). Approximating n-player behavioural strategy nash equilibria using coevolution
Samothrakis, S., Rob, D. and Lucas, SM., (2010). A UCT agent for Tron: Initial investigations
Samothrakis, S. and Lucas, SM., (2010). Planning using online evolutionary overfitting
Grants and funding
2018
Discovering Individual and Social Preferences through Inverse Reinforcement Learning
Economic and Social Research Council
2017
Embedding a Machine Learning capability into the Hood Group Ltd platform.
Innovate UK (formerly Technology Stategy Board)
Create new methods of capturing insight from current and future Preqin datasets by embedding AI and Machine Learning techniques across the unique Preqin investor platform.
Prequin
To embed a NLP capability in Objective IT
Innovate UK (formerly Technology Stategy Board)
The project investigates the use of algorithms (genetic + reinforcement) to provide accurate forecasts of asset prices.
Innovate UK (formerly Technology Stategy Board)
Create new methods of capturing insight from current and future Preqin datasets by embedding AI and Machine Learning techniques across the unique Preqin investor platform.
Prequin
2016
67% Embedding an innovative application of advanced data mining, data analytics and data visualisation to exploit the growth potential of the UK's leading insight platform for professional services firms
Technology STrategy Board
33% Embedding an innovative application of advanced data mining, data analytics and data visualisation to exploit the growth potential of the UK's leading insight platform for professional services firms
Mondaq Ltd
67% - The design and development of a scalable, avatar based, digital healthcare platform, driven by AI and Machine Learning technology.
Technology STrategy Board
33% - The design and development of a scalable, avatar based, digital healthcare platform, driven by AI and Machine Learning technology.
Orbital Media & Advertising Ltd.
Scoping Exercise for new data product
Hood Group Ltd
2015
67% - To extend the business intelligence and digital marketing offer by developing and embedding a new data analytics capability
Technology STrategy Board
33% - To extend the business intelligence and digitial marketing offer by developing and embedding a new data analytics capability
Objective Computing Ltd